4  scTCRseq Skin: Attach scTCR and categorizations to scRNA object

4.1 Set up workspace

# Libraries
library(tidyverse)

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library(Seurat)

Loading required package: SeuratObject
Loading required package: sp

Attaching package: 'SeuratObject'

The following objects are masked from 'package:base':

    intersect, t

library(dplyr)
library(scRepertoire)
library(data.table)

Attaching package: 'data.table'

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    hour, isoweek, mday, minute, month, quarter, second, wday, week,
    yday, year

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    between, first, last

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    transpose

4.2 Load scRepertoire skin and tumor data with my custom vjaa column

combined_TCR <- readRDS("sctcr_scRep_combined_TCR_skin_tumor_Part1.Rds")

4.3 Load categorizations of scTCR clones

p101_skin <- read.csv("sctcr_scRep_p101_skin_freq_Part2.csv")
p103_skin <- read.csv("sctcr_scRep_p103_skin_freq_Part2.csv")
p104_skin <- read.csv("sctcr_scRep_p104_skin_freq_Part2.csv")
p105_skin <- read.csv("sctcr_scRep_p105_skin_freq_Part2.csv")
p106_skin <- read.csv("sctcr_scRep_p106_skin_freq_Part2.csv")
p108_skin <- read.csv("sctcr_scRep_p108_skin_freq_Part2.csv")
p109_skin <- read.csv("sctcr_scRep_p109_skin_freq_Part2.csv")
p110_skin <- read.csv("sctcr_scRep_p110_skin_freq_Part2.csv")
p111_skin <- read.csv("sctcr_scRep_p111_skin_freq_Part2.csv")

4.4 Reformat dataframes so that each scTCR barcode has its own row

p101_skin_reformatted <- data.frame()
for(i in c(1:nrow(p101_skin))){
  tcr_categories <- p101_skin[i, c("sctcr_category", "sctcr_category2", "sctcr_category3", "reactive")]
  barcodes <- p101_skin[i,"barcodes"]
  barcodes <- str_split_1(barcodes, ",")
  reformatted_df <- as.data.frame(barcodes)
  reformatted_df <- cbind(reformatted_df, tcr_categories)
  p101_skin_reformatted <- rbind(p101_skin_reformatted, reformatted_df)
}

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rownames(p101_skin_reformatted) <- p101_skin_reformatted$barcodes
  
p103_skin_reformatted <- data.frame()
for(i in c(1:nrow(p103_skin))){
  tcr_categories <- p103_skin[i, c("sctcr_category", "sctcr_category2", "sctcr_category3", "reactive")]
  barcodes <- p103_skin[i,"barcodes"]
  barcodes <- str_split_1(barcodes, ",")
  reformatted_df <- as.data.frame(barcodes)
  reformatted_df <- cbind(reformatted_df, tcr_categories)
  p103_skin_reformatted <- rbind(p103_skin_reformatted, reformatted_df)
}

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rownames(p103_skin_reformatted) <- p103_skin_reformatted$barcodes

p104_skin_reformatted <- data.frame()
for(i in c(1:nrow(p104_skin))){
  tcr_categories <- p104_skin[i, c("sctcr_category", "sctcr_category2", "sctcr_category3", "reactive")]
  barcodes <- p104_skin[i,"barcodes"]
  barcodes <- str_split_1(barcodes, ",")
  reformatted_df <- as.data.frame(barcodes)
  reformatted_df <- cbind(reformatted_df, tcr_categories)
  p104_skin_reformatted <- rbind(p104_skin_reformatted, reformatted_df)
}

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rownames(p104_skin_reformatted) <- p104_skin_reformatted$barcodes

p105_skin_reformatted <- data.frame()
for(i in c(1:nrow(p105_skin))){
  tcr_categories <- p105_skin[i, c("sctcr_category", "sctcr_category2", "sctcr_category3", "reactive")]
  barcodes <- p105_skin[i,"barcodes"]
  barcodes <- str_split_1(barcodes, ",")
  reformatted_df <- as.data.frame(barcodes)
  reformatted_df <- cbind(reformatted_df, tcr_categories)
  p105_skin_reformatted <- rbind(p105_skin_reformatted, reformatted_df)
}

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rownames(p105_skin_reformatted) <- p105_skin_reformatted$barcodes

p106_skin_reformatted <- data.frame()
for(i in c(1:nrow(p106_skin))){
  tcr_categories <- p106_skin[i, c("sctcr_category", "sctcr_category2", "sctcr_category3", "reactive")]
  barcodes <- p106_skin[i,"barcodes"]
  barcodes <- str_split_1(barcodes, ",")
  reformatted_df <- as.data.frame(barcodes)
  reformatted_df <- cbind(reformatted_df, tcr_categories)
  p106_skin_reformatted <- rbind(p106_skin_reformatted, reformatted_df)
}

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rownames(p106_skin_reformatted) <- p106_skin_reformatted$barcodes

p108_skin_reformatted <- data.frame()
for(i in c(1:nrow(p108_skin))){
  tcr_categories <- p108_skin[i, c("sctcr_category", "sctcr_category2", "sctcr_category3", "reactive")]
  barcodes <- p108_skin[i,"barcodes"]
  barcodes <- str_split_1(barcodes, ",")
  reformatted_df <- as.data.frame(barcodes)
  reformatted_df <- cbind(reformatted_df, tcr_categories)
  p108_skin_reformatted <- rbind(p108_skin_reformatted, reformatted_df)
}

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rownames(p108_skin_reformatted) <- p108_skin_reformatted$barcodes

p109_skin_reformatted <- data.frame()
for(i in c(1:nrow(p109_skin))){
  tcr_categories <- p109_skin[i, c("sctcr_category", "sctcr_category2", "sctcr_category3", "reactive")]
  barcodes <- p109_skin[i,"barcodes"]
  barcodes <- str_split_1(barcodes, ",")
  reformatted_df <- as.data.frame(barcodes)
  reformatted_df <- cbind(reformatted_df, tcr_categories)
  p109_skin_reformatted <- rbind(p109_skin_reformatted, reformatted_df)
}

Warning in data.frame(..., check.names = FALSE): row names were found from a
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rownames(p109_skin_reformatted) <- p109_skin_reformatted$barcodes

p110_skin_reformatted <- data.frame()
for(i in c(1:nrow(p110_skin))){
  tcr_categories <- p110_skin[i, c("sctcr_category", "sctcr_category2", "sctcr_category3", "reactive")]
  barcodes <- p110_skin[i,"barcodes"]
  barcodes <- str_split_1(barcodes, ",")
  reformatted_df <- as.data.frame(barcodes)
  reformatted_df <- cbind(reformatted_df, tcr_categories)
  p110_skin_reformatted <- rbind(p110_skin_reformatted, reformatted_df)
}

Warning in data.frame(..., check.names = FALSE): row names were found from a
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rownames(p110_skin_reformatted) <- p110_skin_reformatted$barcodes

p111_skin_reformatted <- data.frame()
for(i in c(1:nrow(p111_skin))){
  tcr_categories <- p111_skin[i, c("sctcr_category", "sctcr_category2", "sctcr_category3", "reactive")]
  barcodes <- p111_skin[i,"barcodes"]
  barcodes <- str_split_1(barcodes, ",")
  reformatted_df <- as.data.frame(barcodes)
  reformatted_df <- cbind(reformatted_df, tcr_categories)
  p111_skin_reformatted <- rbind(p111_skin_reformatted, reformatted_df)
}

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rownames(p111_skin_reformatted) <- p111_skin_reformatted$barcodes

4.5 Merge categories and reactivity per barcode into one dataframe

skin_category_reactivity <- rbindlist(list(p101_skin_reformatted, p103_skin_reformatted, p104_skin_reformatted, p105_skin_reformatted, p106_skin_reformatted, p108_skin_reformatted, p109_skin_reformatted, p110_skin_reformatted, p111_skin_reformatted), fill = TRUE)

# Add barcode
rownames(skin_category_reactivity) <- skin_category_reactivity$barcode

4.6 Open latest scRNA object

skin.18279 <- readRDS("/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/Analysis_072324_Skin/Melanoma_18-279_SKIN_scRNA_USA_alevin-fry_seurat_integrated_harmony_clustered_withSexes_TNKsubClustered_withProjecTILs_CD4_CD8.rds")

4.7 Link scTCR data to scRNA data using custom column

combined_TCR$P101_Skin_Post3rd$barcode[1:10]

 [1] "P101_Skin_Post3rd_AAACCTGTCGTCTGCT-1"
 [2] "P101_Skin_Post3rd_AAACGGGAGCACAGGT-1"
 [3] "P101_Skin_Post3rd_AAACGGGCAAGCCATT-1"
 [4] "P101_Skin_Post3rd_AAACGGGCACATCTTT-1"
 [5] "P101_Skin_Post3rd_AAACGGGGTCCCGACA-1"
 [6] "P101_Skin_Post3rd_AAAGCAAGTTAAAGAC-1"
 [7] "P101_Skin_Post3rd_AAAGTAGTCTCAAGTG-1"
 [8] "P101_Skin_Post3rd_AAATGCCGTCGAATCT-1"
 [9] "P101_Skin_Post3rd_AACACGTAGTGTACTC-1"
[10] "P101_Skin_Post3rd_AACCATGAGGATTCGG-1"

colnames(skin.18279)[1:10]

 [1] "P101_Skin_Pre3rd_2.5mgIpi_RNA_GCACATAAGTGCCATT"
 [2] "P101_Skin_Pre3rd_2.5mgIpi_RNA_GGAACTTGTCGCGGTT"
 [3] "P101_Skin_Pre3rd_2.5mgIpi_RNA_TCTATTGGTTAGATGA"
 [4] "P101_Skin_Pre3rd_2.5mgIpi_RNA_TGAAAGAAGGTGACCA"
 [5] "P101_Skin_Pre3rd_2.5mgIpi_RNA_GCAATCAGTCCGTCAG"
 [6] "P101_Skin_Pre3rd_2.5mgIpi_RNA_GCATGTAAGTCCGTAT"
 [7] "P101_Skin_Pre3rd_2.5mgIpi_RNA_TTCTTAGAGATGCCAG"
 [8] "P101_Skin_Pre3rd_2.5mgIpi_RNA_GATCAGTCACATTAGC"
 [9] "P101_Skin_Pre3rd_2.5mgIpi_RNA_CTACGTCGTCTCCATC"
[10] "P101_Skin_Pre3rd_2.5mgIpi_RNA_GACTGCGAGATGTCGG"

colnames(skin.18279) <- str_replace_all(str_replace_all(colnames(skin.18279),"_.{0,2}5mgIpi_RNA",""),"$","-1")
colnames(skin.18279)[1:10]

 [1] "P101_Skin_Pre3rd_GCACATAAGTGCCATT-1" "P101_Skin_Pre3rd_GGAACTTGTCGCGGTT-1"
 [3] "P101_Skin_Pre3rd_TCTATTGGTTAGATGA-1" "P101_Skin_Pre3rd_TGAAAGAAGGTGACCA-1"
 [5] "P101_Skin_Pre3rd_GCAATCAGTCCGTCAG-1" "P101_Skin_Pre3rd_GCATGTAAGTCCGTAT-1"
 [7] "P101_Skin_Pre3rd_TTCTTAGAGATGCCAG-1" "P101_Skin_Pre3rd_GATCAGTCACATTAGC-1"
 [9] "P101_Skin_Pre3rd_CTACGTCGTCTCCATC-1" "P101_Skin_Pre3rd_GACTGCGAGATGTCGG-1"

# Bugs out when you use capital letters in your custom call
skin.18279 <- combineExpression(combined_TCR, 
                         skin.18279, 
                         cloneCall="vjaa", 
                         group.by = "sample", 
                         proportion = TRUE)

Warning in .convertClonecall(x): A custom variable vjaa will be used to call
clones

4.8 Attach categories into metadata

skin.18279 <- AddMetaData(skin.18279, skin_category_reactivity)

4.9 Save scRNA object with all TCRs formatted via scRep and TCR reactivities

saveRDS(skin.18279, "scRNA_TCR_Skin_Part3.Rds")

4.10 Get session info

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.10 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.15.so;  LAPACK version 3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/New_York
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] data.table_1.15.4  scRepertoire_2.0.0 Seurat_5.1.0       SeuratObject_5.0.2
 [5] sp_2.2-0           lubridate_1.9.4    forcats_1.0.0      stringr_1.5.1     
 [9] dplyr_1.1.4        purrr_1.0.4        readr_2.1.5        tidyr_1.3.1       
[13] tibble_3.2.1       ggplot2_3.5.1      tidyverse_2.0.0   

loaded via a namespace (and not attached):
  [1] cubature_2.1.1              RcppAnnoy_0.0.22           
  [3] splines_4.3.2               later_1.4.1                
  [5] bitops_1.0-9                R.oo_1.27.0                
  [7] polyclip_1.10-7             fastDummies_1.7.5          
  [9] lifecycle_1.0.4             globals_0.16.3             
 [11] lattice_0.22-7              MASS_7.3-60.0.1            
 [13] magrittr_2.0.3              plotly_4.10.4              
 [15] rmarkdown_2.29              remotes_2.5.0              
 [17] httpuv_1.6.15               sctransform_0.4.1          
 [19] flexmix_2.3-20              spam_2.11-1                
 [21] spatstat.sparse_3.1-0       reticulate_1.42.0          
 [23] cowplot_1.1.3               pbapply_1.7-2              
 [25] RColorBrewer_1.1-3          abind_1.4-8                
 [27] zlibbioc_1.48.2             Rtsne_0.17                 
 [29] GenomicRanges_1.54.1        R.utils_2.13.0             
 [31] ggraph_2.2.1                BiocGenerics_0.48.1        
 [33] RCurl_1.98-1.17             hash_2.2.6.3               
 [35] nnet_7.3-20                 tweenr_2.0.3               
 [37] evmix_2.12                  GenomeInfoDbData_1.2.11    
 [39] IRanges_2.36.0              S4Vectors_0.40.2           
 [41] ggrepel_0.9.5               irlba_2.3.5.1              
 [43] listenv_0.9.1               spatstat.utils_3.1-0       
 [45] SeuratWrappers_0.3.5        iNEXT_3.0.1                
 [47] MatrixModels_0.5-3          goftest_1.2-3              
 [49] RSpectra_0.16-2             spatstat.random_3.3-1      
 [51] fitdistrplus_1.2-2          parallelly_1.41.0          
 [53] leiden_0.4.3.1              codetools_0.2-20           
 [55] DelayedArray_0.28.0         ggforce_0.4.2              
 [57] tidyselect_1.2.1            farver_2.1.2               
 [59] viridis_0.6.5               matrixStats_1.5.0          
 [61] stats4_4.3.2                spatstat.explore_3.3-2     
 [63] jsonlite_1.8.9              tidygraph_1.3.1            
 [65] progressr_0.15.1            ggridges_0.5.6             
 [67] ggalluvial_0.12.5           survival_3.8-3             
 [69] tools_4.3.2                 stringdist_0.9.12          
 [71] ica_1.0-3                   Rcpp_1.0.14                
 [73] glue_1.8.0                  gridExtra_2.3              
 [75] SparseArray_1.2.4           xfun_0.50                  
 [77] MatrixGenerics_1.14.0       GenomeInfoDb_1.38.8        
 [79] withr_3.0.2                 BiocManager_1.30.25        
 [81] fastmap_1.2.0               SparseM_1.84-2             
 [83] rsvd_1.0.5                  digest_0.6.37              
 [85] timechange_0.3.0            R6_2.6.1                   
 [87] mime_0.13                   colorspace_2.1-1           
 [89] scattermore_1.2             tensor_1.5                 
 [91] spatstat.data_3.1-2         R.methodsS3_1.8.2          
 [93] generics_0.1.3              graphlayouts_1.1.1         
 [95] httr_1.4.7                  htmlwidgets_1.6.4          
 [97] S4Arrays_1.2.1              uwot_0.2.3                 
 [99] pkgconfig_2.0.3             gtable_0.3.6               
[101] modeltools_0.2-23           lmtest_0.9-40              
[103] SingleCellExperiment_1.24.0 XVector_0.42.0             
[105] htmltools_0.5.8.1           dotCall64_1.2              
[107] scales_1.3.0                Biobase_2.62.0             
[109] png_0.1-8                   spatstat.univar_3.0-0      
[111] ggdendro_0.2.0              knitr_1.49                 
[113] rstudioapi_0.17.1           rjson_0.2.23               
[115] tzdb_0.5.0                  reshape2_1.4.4             
[117] nlme_3.1-168                zoo_1.8-13                 
[119] cachem_1.1.0                KernSmooth_2.23-26         
[121] parallel_4.3.2              miniUI_0.1.1.1             
[123] pillar_1.10.1               grid_4.3.2                 
[125] vctrs_0.6.5                 RANN_2.6.2                 
[127] VGAM_1.1-13                 promises_1.3.2             
[129] xtable_1.8-4                cluster_2.1.8.1            
[131] evaluate_1.0.1              truncdist_1.0-2            
[133] cli_3.6.3                   compiler_4.3.2             
[135] rlang_1.1.5                 crayon_1.5.3               
[137] future.apply_1.11.3         plyr_1.8.9                 
[139] stringi_1.8.4               viridisLite_0.4.2          
[141] deldir_2.0-4                munsell_0.5.1              
[143] gsl_2.1-8                   lazyeval_0.2.2             
[145] spatstat.geom_3.3-2         quantreg_6.1               
[147] Matrix_1.6-5                RcppHNSW_0.6.0             
[149] hms_1.1.3                   patchwork_1.3.0            
[151] future_1.34.0               shiny_1.9.1                
[153] SummarizedExperiment_1.32.0 evd_2.3-7.1                
[155] ROCR_1.0-11                 igraph_2.0.3               
[157] memoise_2.0.1